125 research outputs found
A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
Identifying university students' weaknesses results in better learning and
can function as an early warning system to enable students to improve. However,
the satisfaction level of existing systems is not promising. New and dynamic
hybrid systems are needed to imitate this mechanism. A hybrid system (a
modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used
to forecast students' outcomes. This proposed system would improve instruction
by the faculty and enhance the students' learning experiences. The results show
that a modified recurrent neural network with an adapted Grey Wolf Optimizer
has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON
VAPI: Vectorization of Algorithm for Performance Improvement
This study presents the vectorization of metaheuristic algorithms as the
first stage of vectorized optimization implementation. Vectorization is a
technique for converting an algorithm, which operates on a single value at a
time to one that operates on a collection of values at a time to execute
rapidly. The vectorization technique also operates by replacing multiple
iterations into a single operation, which improves the algorithm's performance
in speed and makes the algorithm simpler and easier to be implemented. It is
important to optimize the algorithm by implementing the vectorization
technique, which improves the program's performance, which requires less time
and can run long-running test functions faster, also execute test functions
that cannot be implemented in non-vectorized algorithms and reduces iterations
and time complexity. Converting to vectorization to operate several values at
once and enhance algorithms' speed and efficiency is a solution for long
running times and complicated algorithms. The objective of this study is to use
the vectorization technique on one of the metaheuristic algorithms and compare
the results of the vectorized algorithm with the algorithm which is
non-vectorized.Comment: 21 page
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
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